# SET INPUT AND OUTPUT PATH
# Using function here() wil provide path to the OCR_Pipeline(package) folder on your computer,
# the input and output separate folers are in "/OCR_Pipeline/Data". 
# Recomend to move the result to different folder before every analysis.
#
# To choose different folder in your coputer as I/O folder write entire Path to the folder. 
INPUT_PATH  <- here('/Data/INPUT')
OUTPUT_PATH <- here('/Data/OUTPUT')

OCR

Identyfy Outliars

Removed outliars in each iteration:

## 1  Well outliares:  788 -- 11.52047 % 
## 2  Well outliares:  296 -- 4.327485 % 
## 3  Well outliares:  33 -- 0.4824561 % 
## 4  Well outliares:  0 -- 0 % 
## 1  Point outliares:  158 -- 2.839684 % 
## 2  Point outliares:  58 -- 1.042416 % 
## 3  Point outliares:  30 -- 0.5391804 % 
## 4  Point outliares:  17 -- 0.3055356 % 
## 5  Point outliares:  4 -- 0.07189073 % 
## 6  Point outliares:  1 -- 0.01797268 % 
## 7  Point outliares:  1 -- 0.01797268 % 
## 8  Point outliares:  0 -- 0 % 
## Tolat well outliars:  16.71905 % 
## Tolat single point outliars:  4.026343 %

Procentage of all outliars across samples

TO DO: convert graph to aditive add legends

Interval estimates

Int1 Int2 Int3 Int4 Sample norm.Int1 norm.Int2 norm.Int3 norm.Int4
4.103 3.136 4.959 2.870 24-post-0 60.503 23.022 142.478 17.633
2.908 1.809 3.407 1.670 24-pre-1 18.325 6.104 30.186 5.311
4.080 3.087 5.162 2.962 31 post-2 59.174 21.902 174.572 19.341
4.822 3.863 6.185 3.638 42 pre-2 124.157 47.601 485.624 38.024
3.504 2.557 4.414 2.333 33-pre-3 33.257 12.894 82.559 10.313
3.941 2.905 4.831 2.869 34-post-4 51.466 18.267 125.330 17.618
3.388 2.460 3.982 2.051 34-pre-5 29.606 11.702 53.603 7.779
3.696 2.920 4.643 2.745 S-36pre-6 40.266 18.539 103.828 15.569
3.643 2.801 4.546 2.663 S-43pre-6 38.194 16.458 94.262 14.335
3.609 2.682 4.400 2.481 36-pre-7 36.924 14.616 81.438 11.950
3.594 2.472 4.677 2.363 39-post-8 36.378 11.844 107.475 10.622
3.586 2.409 4.564 2.428 39-pre-9 36.084 11.123 95.934 11.334
2.993 1.586 3.949 1.559 42 post-10 19.948 4.884 51.885 4.756
3.403 1.894 3.931 1.891 #46 pre-10 30.043 6.645 50.977 6.629
3.845 2.889 4.616 2.800 43 post-11 46.750 17.981 101.051 16.438
3.756 2.930 4.437 2.763 49 pre-11 42.772 18.719 84.496 15.846
3.979 2.946 4.883 2.708 46 post-12 53.439 19.028 131.984 15.001
3.792 2.688 4.792 2.331 40 pre-12 44.353 14.700 120.512 10.291
3.555 2.754 4.378 2.942 49-post-13 34.981 15.712 79.673 18.958

Error of esstimates

Int1 Int2 Int3 Int4 Sample
0.013 0.016 0.020 0.015 24-post-0
0.026 0.032 0.043 0.032 24-pre-1
0.009 0.012 0.016 0.012 31 post-2
0.011 0.015 0.018 0.013 42 pre-2
0.013 0.018 0.021 0.017 33-pre-3
0.016 0.020 0.031 0.019 34-post-4
0.039 0.049 0.063 0.048 34-pre-5
0.017 0.022 0.026 0.020 S-36pre-6
0.024 0.029 0.040 0.029 S-43pre-6
0.014 0.017 0.022 0.016 36-pre-7
0.031 0.036 0.045 0.037 39-post-8
0.016 0.020 0.028 0.020 39-pre-9
0.042 0.060 0.071 0.061 42 post-10
0.038 0.052 0.059 0.046 #46 pre-10
0.014 0.018 0.024 0.017 43 post-11
0.013 0.015 0.022 0.015 49 pre-11
0.021 0.026 0.031 0.025 46 post-12
0.018 0.022 0.029 0.022 40 pre-12
0.016 0.022 0.026 0.019 49-post-13 

## Using Sample as id variables

Bioenergetics

## Using Sample as id variables

## Using Sample as id variables

========================= ECAR =========================

Remove outliars

## 1  Well outliares:  339 -- 7.080201 % 
## 2  Well outliares:  107 -- 2.234754 % 
## 3  Well outliares:  93 -- 1.942356 % 
## 4  Well outliares:  49 -- 1.023392 % 
## 5  Well outliares:  49 -- 1.023392 % 
## 6  Well outliares:  12 -- 0.2506266 % 
## 7  Well outliares:  0 -- 0 % 
## 1  Point outliares:  104 -- 2.567901 % 
## 2  Point outliares:  19 -- 0.4691358 % 
## 3  Point outliares:  10 -- 0.2469136 % 
## 4  Point outliares:  0 -- 0 % 
## Tolat well outliars:  13.81145 % 
## Tolat single point outliars:  2.830389 %

for (smpl in unique(dr_ecar$sample_id)) {
  plot <- ggplot(filter(dr_ecar, sample_id == smpl), aes(Measurement, LECAR))+
            ggtitle(paste0("SAMPLE:  ", smpl))+
            geom_line(aes(group = Well),size = 0.2, color = "grey") +
            geom_point(aes(Measurement, LECAR, color = out))+
            xlab("Interval")#+
            #facet_grid(Protocol  ~ . ) 
  
  print(plot +theme_bw())
}

Make clear separation of those two ECAR vs OCR # Estimates and bionergetics

Interval estimates

Int1 Int2 Int5 Sample norm.Int1 norm.Int2 norm.Int5
3.805 4.216 3.051 24-post-0 44.931 67.744 21.129
3.098 3.333 2.267 24-pre-1 22.153 28.011 9.651
3.486 3.890 2.854 31 post-2 32.660 48.893 17.360
3.884 4.408 3.435 42 pre-2 48.606 82.102 31.033
3.285 3.654 2.600 33-pre-3 26.709 38.630 13.461
3.443 3.996 2.870 34-post-4 31.265 54.388 17.645
3.206 3.622 2.568 34-pre-5 24.676 37.410 13.044
3.182 3.543 2.543 S-36pre-6 24.083 34.578 12.718
3.330 3.782 2.734 S-43pre-6 27.945 43.898 15.395
3.401 3.673 2.489 36-pre-7 29.993 39.375 12.049
3.644 3.939 2.802 39-post-8 38.261 51.386 16.484
3.621 3.897 2.786 39-pre-9 37.384 49.273 16.217
3.240 3.440 2.464 42 post-10 25.541 31.193 11.754
3.603 3.967 2.786 #46 pre-10 36.695 52.837 16.213
3.258 3.746 2.626 43 post-11 26.001 42.334 13.814
3.210 3.620 2.430 49 pre-11 24.790 37.344 11.364
3.480 4.160 2.995 46 post-12 32.451 64.071 19.989
3.123 3.820 2.927 40 pre-12 22.703 45.614 18.665
3.219 3.509 2.241 49-post-13 25.003 33.432 9.403

Error of esstimates

Int1 Int2 Int5 Sample
0.008 0.011 0.018 24-post-0
0.007 0.009 0.015 24-pre-1
0.009 0.012 0.020 31 post-2
0.009 0.011 0.018 42 pre-2
0.007 0.010 0.014 33-pre-3
0.012 0.017 0.028 34-post-4
0.007 0.010 0.015 34-pre-5
0.010 0.013 0.023 S-36pre-6
0.010 0.012 0.020 S-43pre-6
0.008 0.010 0.016 36-pre-7
0.010 0.012 0.019 39-post-8
0.007 0.009 0.015 39-pre-9
0.010 0.015 0.025 42 post-10
0.008 0.011 0.023 #46 pre-10
0.008 0.011 0.017 43 post-11
0.008 0.010 0.020 49 pre-11
0.009 0.012 0.020 46 post-12
0.009 0.011 0.019 40 pre-12
0.009 0.011 0.021 49-post-13 

## Using Sample as id variables

Bioenergetics

## Using Sample as id variables

## Using Sample as id variables